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Maleknaz Nayebi, assistant professor in the Department of Electrical Engineering and Computer Science at York University’s Lassonde School of Engineering, received a Distinguished Paper Award from the Institute of Electrical and Electronics Engineers (IEEE) Technical Community on Software Engineering in recognition of work on app system function and user satisfaction.
Nayebi received the award at the 31st IEEE International Requirements Engineering 2023 Conference for her research paper titled "User Driven Functionality Deletion for Mobile Apps."
The paper builds upon Nayebi’s ongoing work to develop a stronger understanding of the needs and preferences of software users through techniques such as data mining and population studies, to challenge conventional laws of software engineering and improve user experience and system function.
“Software products are governed by a law of growth,” she says. “We are told that offering more and adding new features to software applications will help keep customers satisfied.”
This law, known as Lehman’s Law of Growth, has long served as a fundamental principle in software evolution. Nayebi is questioning this rule by presenting compelling empirical evidence that highlights its inaccuracies.
“Our research showed that against Lehman’s Law of Growth and common beliefs, the functionality of software applications and particularly mobile apps can actually decrease overtime. This is why we explored ways to remove unnecessary features without affecting the experience of users,” she says.
Though researchers are beginning to understand the advantages of removing specific features from software applications, the ways in which feature deletions impact users are less understood. To bridge this gap, Nayebi conducted various case studies in collaboration with researchers from the University of Calgary and the CISPA Helmholtz Center for Information Security. Information collected from these studies was used to develop RADIATION (Review bAsed DeletIon recommendATION), a recommendation tool that can help developers identify the best software application features to delete, without affecting user experience.
RADIATION applies machine learning methods to scan through different software application reviews from users and identify constructive opinions. In this way, RADIATION can evaluate user perspectives regarding different software application features and determine the best options for removal without provoking negative user feelings.
Nayebi’s research has the potential to be applied across many fields and disciplines. She is currently working with various companies that can use feature removal methods to solve issues with emergency management and e-health software applications, while satisfying the software design preferences of users.